Note. Boxplots display the interquartile range (IQR, center box), and the whiskers extend 1.5*IQR from the lower and upper hinge. The white point indicates the mean and the white center line indicates the median.


Import Data

In a first step we import the raw Qualtrics data, which was downloaded as an SPSS file.

# Reset working directory to folder current file is saved in
#setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

# Import Qualtrics Survey Data
dt0Raw <- read_spss("data/raw data/Snowball+Corona+Long-Term+Coverage+-+Baseline_April+1,+2020_07.26.sav")

The raw data set includes 660 variables for 22140 cases.

Data Quality

Filter: Preview Responses

Filter the Preview responses.

# flag Preview Responses
dt1Preview <- dt0Raw %>%
  mutate(FilterPreview = labelled(ifelse(Status == 0,0,1),
                                  labels = c(preview = 1), label="Filter: survey preview response"))

Filter: Survey Progress (drop out)

Inspecting missing data in the items.

# Table: Missing Data per item
dt1Preview %>%
  dplyr::select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  dplyr::select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::miss_var_summary(.) %>% # by variable summary of missingness proportion
  DT::datatable(.,
                colnames = c("Variable", "Number Missing", "Percentage Missing"),
                filter = 'top',
                extensions = 'Buttons',
                options = list(
                  columnDefs = list(list(className = 'dt-center')),
                  #autoWidth = TRUE,
                  dom = 'Bfrtlip',
                  buttons = c('copy', 'csv', 'excel', 'pdf', 'print'))) %>%
  DT::formatRound('pct_miss', digits = 2)
# Plot: Missing Data per item
dt1Preview %>%
  dplyr::select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  dplyr::select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::gg_miss_var(.) # visualize by variable summary of missingness proportion

# Plot: Missing Data cumulative
dt1Preview %>%
  dplyr::select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  dplyr::select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::gg_miss_var_cumsum(.) # missingness development over survey

# Co-occurences of missingess - too many variables
#dt0Raw %>%
#  dplyr::select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
#  dplyr::select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
#  naniar::gg_miss_upset(., nsets = n_var_miss(.)) # visualize missingess co-occurences

# set time cut-off criterion:
progressCutOff <- 97 #cut-off criterion in percent
progressFilter <- dt1Preview %>%
  dplyr::select(Progress) %>%
  mutate(out = Progress < progressCutOff)

progressCutOffPerc <- round(sum(progressFilter$out)/nrow(progressFilter)*100,2) # percent of missing data with current cut-off criterion

# plot histogram and missing
ggplot(data=progressFilter, aes(x=Progress, fill=out)) +
  geom_histogram(bins=50,
                 alpha=.6) +
  geom_vline(xintercept = progressCutOff, 
             color = "darkred",
             linetype = "longdash") +
  geom_text(aes(x=progressCutOff, label=paste0("Progress cut-off: ",progressCutOff,"%\n"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  geom_text(aes(x=progressCutOff, label=paste0("\ndata loss: ",progressCutOffPerc,"%"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  #scale_x_continuous(breaks = seq(0, 100,3)) +
  scale_fill_manual(values=c("darkgrey","darkred")) +
  labs(title = "Histogram: Survey Progress", 
       x = "Survey Progress [Percent completed]",
       y = "Frequency Count") +
  theme_Publication() +
  theme(legend.position = "none")

# flag anyone with less than 5 minutes survey duration
dt2Progress <- dt1Preview %>%
  mutate(FilterProgress = labelled(ifelse(Progress < progressCutOff,1,0),
                               labels = c(`consent` = 1), label="Filter: Did not see debriefing"))

rm(progressFilter, progressCutOff, progressCutOffPerc)

Filter: Short Duration on Survey

Filter survey responses that were shorter than 5 minutes.

# truncate data:
tOutlierHigh <- dt2Progress %>%
  dplyr::select(Duration__in_seconds_) %>%
  filter(Duration__in_seconds_<=stats::median(Duration__in_seconds_)+stats::mad(Duration__in_seconds_)*3.5) %>%
  mutate(Minutes = Duration__in_seconds_/60)

# set time cut-off criterion:
tCutOff <- 5 #cut-off criterion in minutes
# CJ: This might be a bit strict, I suspect that I completed it in <10 minutes. 
tCutOffPerc <- round(sum(tOutlierHigh$Minutes<tCutOff)/nrow(dt2Progress)*100,2) # percent of missing data with current cut-off criterion
tOutlierHigh$out <- tOutlierHigh$Minutes < tCutOff

# plot histogram and missing
ggplot(data=tOutlierHigh, aes(x=Minutes, fill=out)) +
  geom_histogram(bins=round(max(tOutlierHigh$Minutes),0),
                 alpha=.6) +
  geom_vline(xintercept = tCutOff, 
             color = "darkred",
             linetype = "longdash") +
  geom_text(aes(x=tCutOff, label=paste0("time cut-off: ",tCutOff," Minutes\n"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  geom_text(aes(x=tCutOff, label=paste0("\ndata loss: ",tCutOffPerc,"%"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  scale_x_continuous(breaks = seq(0, round(max(tOutlierHigh$Minutes),0), 5)) +
  scale_fill_manual(values=c("darkgrey","darkred")) +
  labs(title = "Truncated Histogram: Survey Duration", 
       x = "Duration [Mintues]",
       y = "Frequency Count",
       caption = "Notes:
       (1) Truncated: all participants who took less time than Median+3.5*MAD
       (2) Each bin represents one Minute") +
  theme_Publication() +
  theme(legend.position = "none")

# flag anyone with less than 5 minutes survey duration
dt3Time <- dt2Progress %>%
  mutate(FilterTime = labelled(ifelse(Duration__in_seconds_ > tCutOff*60,0,1),
                               labels = c(`extremely quick` = 1), label="Filter: Took less than 5 minutes on survey"))

rm(tOutlierHigh, tCutOff, tCutOffPerc)

Filter: Straightliners

Filter participants, who have straightlined on the job insecurity scale, which includes a reverse coded item. We only flag people who straightlined outside the median categories because all “neither agree nor disagree” might be meaningful response.

# CheckMissingness pattern
naniar::gg_miss_upset(dt3Time %>%
  dplyr::select(ResponseId, jbInsec01, jbInsec02, jbInsec03) %>%
  na_if(., -99) # all -99 into <NA>
)

# isolate respondents who have straightlined outside a the median categories (b/c all "neither agree nor disagree" might be meaningful response) 
jobinsecRed <- dt3Time %>%
  dplyr::select(ResponseId, jbInsec01, jbInsec02, jbInsec03) %>%
  na_if(., -99) %>% # all -99 into <NA>
  na.omit() %>% # remove people who have missing data on one of the three items
  mutate(mean = rowMeans(dplyr::select(., c("jbInsec01", "jbInsec02", "jbInsec03"))), 
         sd = matrixStats::rowSds(as.matrix(dplyr::select(., c("jbInsec01", "jbInsec02", "jbInsec03"))))) %>% # calculate row-means and row-sds 
  filter(sd == 0, mean != 0)

# flag anyone who straightlined on job insecurity
dt4Straightliner <- dt3Time %>%
  mutate(FilterStraightliner = labelled(ifelse(!ResponseId %in% jobinsecRed$ResponseId,0,1),
                                        labels = c(straightliner = 1), label="Filter: straightliner on Job Insecurity"))
rm(jobinsecRed)

Data Preparation

Note: For each of the scales we do an item analysis, and combine the items to the mean- (.m), and factor scores (.fa). We also centered (.c) and standardized (.z) the mean scores. Most of these items are not labelled for SPSS yet.

Recoded Items

Re-coding reverse coded items and the Qualtrics language codes.

# Recoded Items
dt5newVars <- dt4Straightliner %>%
  mutate(jbInsec02_R = labelled(recode(as.numeric(jbInsec02), `-2` = 2, `-1` = 1, `0` = 0, `1` = -1, `2` = -2, `-99` = -99),
                                labels = NULL, label="Job Insecurity 02 (re-coded)"),
         bor03_R = labelled(recode(as.numeric(bor03), `-2` = 2, `-1` = 1, `0` = 0, `1` = -1, `2` = -2),
                            labels = NULL, label="Boredom 03 (re-coded)"))
# Language
# Import Qualtrics Language Codes
qualtricsLanguage <- read_excel("data/raw data/qualtricsLanguageCodes.xlsx")
dt5newVars <- merge(x=dt5newVars, y=qualtricsLanguage, by="Q_Language", all.x=TRUE)
rm(qualtricsLanguage)

Country

We currently have 982 different free text country responses. Here we aim to consolidate them into one variable.

# CJ: Just as a benchmark, geolocate IP address. Can always use this if a free 
# CJ: text country response does not resolve.
library(rgeolocate)
file <- system.file("extdata","GeoLite2-Country.mmdb", package = "rgeolocate")
geolocations <- maxmind(dt5newVars$IPAddress, file, c("continent_name", "country_code", "country_name"))
dt5newVars$coded_country_ip <- geolocations$country_name

# CJ: This is a slightly cleaner, faster, and more informative regex based approach.
# CJ: The function and dictionary (based on the code below, some errors corrected) 
# CJ: is in source("./scripts/functions/dictionary_functions.R")
country_matches <- cat_words(tolower(dt5newVars$country), country_dict)
# Check duplicates, and which regular expressions triggered them
invisible(country_matches$dup)
# Check unmatched strings, fix common ones
invisible(head(country_matches$unmatched))
dt5newVars$coded_country_cj <- country_matches$words

#code originally provided by Courtney Soderberg

dt5newVars <- dt5newVars %>%
  mutate(coded_country = case_when(grepl('^usa$', country, ignore.case = T) | grepl('unites state', country, ignore.case = T) | 
                                     grepl('united state', country, ignore.case = T) | grepl('^america$', country, ignore.case = T) |
                                     grepl('U\\.S\\.', country, ignore.case = T) | grepl('Estados Unidos', country, ignore.case = T) |
                                     grepl('colorado', country, ignore.case = T) | grepl('^us$', country, ignore.case = T) |
                                     grepl('xas', country, ignore.case = T) | grepl('sates', country, ignore.case = T) |
                                     grepl('Amerika Serikat', country, ignore.case = T) | grepl('california', country, ignore.case = T) |
                                     grepl('corlifornia', country, ignore.case = T) | grepl('états-unis', country, ignore.case = T) |
                                     grepl('york', country, ignore.case = T) | grepl('yark', country, ignore.case = T) |
                                     grepl('puerto rico', country, ignore.case = T) | grepl('^tx$', country, ignore.case = T) |
                                     grepl('^tn$', country, ignore.case = T) | grepl('U S', country, ignore.case = T) ~ 'United States of America',
                                   grepl('canad', country, ignore.case = T) | grepl('vancouver', country, ignore.case = T) ~ 'Canada',
                                   grepl('mexico', country, ignore.case = T) | grepl('México', country, ignore.case = T) ~ 'Mexico',
                                   grepl('spain', country, ignore.case = T) | grepl('esp', country, ignore.case = T) |
                                     grepl('Spagna', country, ignore.case = T) | grepl('Spanien', country, ignore.case = T) |
                                     grepl('Catal', country, ignore.case = T) | grepl('Euskal Herria', country, ignore.case = T) |
                                     grepl('basque', country, ignore.case = T) | grepl('Eapaña', country, ignore.case = T) |
                                     grepl('Esapaña', country, ignore.case = T) | grepl('madrid', country, ignore.case = T) |
                                     grepl('Montalbán de Córdoba', country, ignore.case = T) | grepl('Pais vasco', country, ignore.case = T) |
                                     grepl('Spanje', country, ignore.case = T) ~ 'Spain',
                                   grepl('france', country, ignore.case = T) | grepl('Francia', country, ignore.case = T) |
                                     grepl('Frankrijk', country, ignore.case = T) ~ 'France',
                                   grepl('germany', country, ignore.case = T) | grepl('deutschland', country, ignore.case = T) | 
                                     grepl('Alemania', country, ignore.case = T) | grepl('germania', country, ignore.case = T) |
                                     grepl('^Almanya$', country, ignore.case = T) | grepl('berlin', country, ignore.case = T) |
                                     grepl('Duitsland', country, ignore.case = T) ~ 'Germany',
                                   grepl('portugal', country, ignore.case = T) ~ 'Portugal',
                                   grepl('weden', country, ignore.case = T) ~ 'Sweden',
                                   grepl('netherland', country, ignore.case = T) | grepl('nederland', country, ignore.case = T) | 
                                     grepl('Niederlande', country, ignore.case = T) | grepl('Belanda', country, ignore.case = T) |
                                     grepl('^NL$', country, ignore.case = T) | grepl('Olanda', country, ignore.case = T) |
                                     grepl('Paesi Bassi', country, ignore.case = T) | grepl('bajos', country, ignore.case = T) |
                                     grepl('Gelderland', country, ignore.case = T) | grepl('Hollanda', country, ignore.case = T) ~ 'Netherlands',
                                   grepl('^indonesia$', country, ignore.case = T) | grepl('indonesian', country, ignore.case = T) | 
                                     grepl('kota Tarakan', country, ignore.case = T) | grepl('Imdonesia', country, ignore.case = T) |
                                     grepl('Indònesia', country, ignore.case = T) | grepl('jakarta', country, ignore.case = T) ~ 'Indonesia',
                                   grepl('ital', country, ignore.case = T) | grepl('Sardegna', country, ignore.case = T) | 
                                     grepl('Bisceglie', country, ignore.case = T) | grepl('Ladispoli', country, ignore.case = T) |
                                     grepl('Castelforte', country, ignore.case = T) | grepl('milano', country, ignore.case = T) |
                                     (grepl('roma', country, ignore.case = T) & !grepl('romania', country, ignore.case = T)) |
                                     grepl('Dorgali', country, ignore.case = T) | grepl('bari', country, ignore.case = T) |
                                     grepl('bologna', country, ignore.case = T) | grepl('Brescia', country, ignore.case = T) |
                                     grepl('Cala gonone', country, ignore.case = T) | grepl('Chieti', country, ignore.case = T) |
                                     grepl('Ferentino', country, ignore.case = T) | grepl('Frosinone', country, ignore.case = T) |
                                     grepl('Gragnano lucca  ', country, ignore.case = T) | grepl('Guidonia', country, ignore.case = T) |
                                     grepl('Itaia', country, ignore.case = T) | grepl('İtalya', country, ignore.case = T) |
                                     grepl('Mareno di Piave', country, ignore.case = T) | grepl('modena', country, ignore.case = T) |
                                     grepl('Pellizzano', country, ignore.case = T) | grepl('Predazzo', country, ignore.case = T) |
                                     grepl('Refrontolo', country, ignore.case = T) | grepl('Cosma e Damiano', country, ignore.case = T) |
                                     grepl('Scalea', country, ignore.case = T) | grepl('Scauri', country, ignore.case = T) |
                                     grepl('Segni', country, ignore.case = T) | grepl('SETTIMO VITTONE', country, ignore.case = T) |
                                     grepl('Susegana', country, ignore.case = T) | grepl('Terralba', country, ignore.case = T) |
                                     grepl('trento', country, ignore.case = T) | grepl('treviso', country, ignore.case = T) |
                                     grepl('Tezze di Piave', country, ignore.case = T) | grepl('Valmontone', country, ignore.case = T) |
                                     grepl('Vergato', country, ignore.case = T) | grepl('veneto', country, ignore.case = T) |
                                     grepl('Gragnano lucca', country, ignore.case = T) ~ 'Italy',
                                   grepl('hong kong', country, ignore.case = T) ~ 'Hong Kong S.A.R.',
                                   grepl('phil', country, ignore.case = T) | grepl('Filipinas', country, ignore.case = T) ~ 'Philippines',
                                   grepl('argentina', country, ignore.case = T) | grepl('arge', country, ignore.case = T) ~ 'Argentina',
                                   grepl('pakistan', country, ignore.case = T) | grepl('Abbottabad', country, ignore.case = T) |
                                     grepl('Peshawar', country, ignore.case = T) ~ 'Pakistan',
                                   grepl('united kingdo', country, ignore.case = T) | grepl('^uk$', country, ignore.case = T) |
                                     grepl('Reino Unido', country, ignore.case = T) | grepl('britain', country, ignore.case = T) |
                                     grepl('Regno Unito', country, ignore.case = T) | grepl('u\\.k\\.', country, ignore.case = T) |
                                     grepl('بريطانيا', country, ignore.case = T) | grepl('the uk', country, ignore.case = T) |
                                     grepl('U K', country, ignore.case = T) | grepl('Verenigd Koninkrijk', country, ignore.case = T) |
                                     grepl('Windsor', country, ignore.case = T) | grepl('scotland', country, ignore.case = T) |
                                     grepl('england', country, ignore.case = T) | grepl('wales', country, ignore.case = T) |
                                     grepl('İngiltere', country, ignore.case = T) | grepl('Northern Ireland', country, ignore.case = T) |
                                     grepl('Egland', country, ignore.case = T) | grepl('^gb$', country, ignore.case = T) |
                                     grepl('N Ireland', country, ignore.case = T) | grepl('Schotland', country, ignore.case = T) |
                                     grepl('Scozia', country, ignore.case = T) ~ 'United Kingdom',
                                   grepl('africa', country, ignore.case = T) | grepl('^SA$', country, ignore.case = T) |
                                     grepl('Sudáfrica', country, ignore.case = T) | grepl('western cape', country, ignore.case = T) ~ 'South Africa',
                                   grepl('^chile$', country, ignore.case = T) ~ 'Chile',
                                   grepl('australia', country, ignore.case = T) | grepl('Austrija', country, ignore.case = T) ~ 'Australia',
                                   grepl('colombia', country, ignore.case = T) ~ 'Colombia',
                                   grepl('turkey', country, ignore.case = T) | grepl('tür', country, ignore.case = T) ~ 'Turkey',
                                   grepl('taiwan', country, ignore.case = T) ~ 'Taiwan',
                                   grepl('^Venezuela$', country, ignore.case = T) ~ 'Venezuela',
                                   grepl('israel', country, ignore.case = T) | grepl('اللد', country, ignore.case = T) |
                                     grepl('اسرائيل', country, ignore.case = T) | grepl('كفر قاسم', country, ignore.case = T) |
                                     grepl('Isreal', country, ignore.case = T) | grepl('רמלה', country, ignore.case = T) ~ 'Israel',
                                   grepl('greece', country, ignore.case = T) | grepl('Grecia', country, ignore.case = T) ~ 'Greece',
                                   grepl('austria', country, ignore.case = T) | grepl('sterreich', country, ignore.case = T) ~ 'Austria',
                                   grepl('new zealand', country, ignore.case = T) | grepl('Neuseeland', country, ignore.case = T) ~ 'New Zealand',
                                   grepl('Tuni', country, ignore.case = T) | grepl('تونس', country, ignore.case = T) ~ 'Tunisia',
                                   grepl('Belg', country, ignore.case = T) | grepl('Bélgica', country, ignore.case = T) ~ 'Belgium',
                                   grepl('China', country, ignore.case = T) ~ 'China',
                                   grepl('cyp', country, ignore.case = T) ~ 'Cyprus',
                                   grepl('Schweiz', country, ignore.case = T) | grepl('Suiza', country, ignore.case = T) |
                                     grepl('Svizzera', country, ignore.case = T) | grepl('Zwitserland', country, ignore.case = T) |
                                     grepl('switzerland', country, ignore.case = T) ~ 'Switzerland',
                                   grepl('United Arab Emirates', country, ignore.case = T) | grepl('uae', country, ignore.case = T) ~ 'United Arab Emirates',
                                   grepl('Croa', country, ignore.case = T) ~ 'Croatia',
                                   grepl('india', country, ignore.case = T) ~ 'India',
                                   grepl('algeri', country, ignore.case = T) | grepl('الجزائر', country, ignore.case = T) |
                                     grepl('Algérie', country, ignore.case = T) ~ 'Algeria',
                                   grepl('bulgaria', country, ignore.case = T) ~ 'Bulgaria',
                                   grepl('Poland', country, ignore.case = T) | grepl('POLONIA', country, ignore.case = T) ~ 'Poland',
                                   grepl('romania', country, ignore.case = T) ~ 'Romania',
                                   grepl('singapore', country, ignore.case = T) ~ 'Singapore',
                                   grepl('Srbija', country, ignore.case = T) | grepl('serbia', country, ignore.case = T) |
                                     grepl('Србија', country, ignore.case = T) ~ 'Republic of Serbia',
                                   grepl('czech', country, ignore.case = T) | grepl('checa', country, ignore.case = T) ~ 'Czech Republic',
                                   grepl('lux', country, ignore.case = T) ~ 'Luxembourg',
                                   grepl('slova', country, ignore.case = T) ~ 'Slovakia',
                                   grepl('brazil', country, ignore.case = T) | grepl('brasil', country, ignore.case = T)~ 'Brazil',
                                   grepl('^ireland$', country, ignore.case = T) | grepl('Irlanda', country, ignore.case = T) ~ 'Ireland',
                                   grepl('japan', country, ignore.case = T) | grepl('Giappone', country, ignore.case = T) |
                                     grepl('Japonya', country, ignore.case = T) ~ 'Japan',
                                   grepl('Malay', country, ignore.case = T) ~ 'Malaysia',
                                   grepl('nigeria', country, ignore.case = T) ~ 'Nigeria',
                                   grepl('Riyad', country, ignore.case = T) | grepl('^Saudi arabia$', country, ignore.case = T) |
                                     grepl('Arabia Saudita', country, ignore.case = T) | grepl('^saudi$', country, ignore.case = T) |
                                     grepl('Kingdom of Saudia arabia', country, ignore.case = T) | grepl('KSA', country, ignore.case = T) |
                                     grepl('k\\.s\\.a', country, ignore.case = T) | grepl('Arabie saoudite', country, ignore.case = T) | 
                                     grepl('الرياض', country, ignore.case = T) | grepl('السعودية', country, ignore.case = T) |
                                     grepl('السعوديه', country, ignore.case = T) ~ 'Saudi Arabia',
                                   grepl('^thailand$', country, ignore.case = T) ~ 'Thailand',
                                   grepl('urug', country, ignore.case = T) ~ 'Uruguay',
                                   grepl('costa', country, ignore.case = T) ~ 'Costa Rica', 
                                   grepl('ecuador', country, ignore.case = T) ~ 'Ecuador',
                                   grepl('finland', country, ignore.case = T) ~ 'Finland', 
                                   grepl('guat', country, ignore.case = T) ~ 'Guatemala',
                                   grepl('iceland', country, ignore.case = T) ~ 'Iceland',
                                   grepl('iraq', country, ignore.case = T) | grepl('العراق', country, ignore.case = T) ~ 'Iraq',
                                   grepl('iran', country, ignore.case = T) ~ 'Iran',
                                   grepl('lebanon', country, ignore.case = T) | grepl('liban', country, ignore.case = T) ~ 'Lebanon',
                                   grepl('norway', country, ignore.case = T) ~ 'Norway',
                                   grepl('palestine', country, ignore.case = T) | grepl('فلسطين ', country, ignore.case = T) |
                                     grepl('^فلسطين$', country, ignore.case = T) | grepl('الرملة', country, ignore.case = T) ~ 'Palestine',
                                   grepl('peru', country, ignore.case = T) ~ 'Peru',
                                   grepl('domin', country, ignore.case = T) ~ 'Dominican Republic',
                                   grepl('albania', country, ignore.case = T) ~ 'Albania',
                                   grepl('andorra', country, ignore.case = T) ~ 'Andorra',
                                   grepl('bahrain', country, ignore.case = T) ~ 'Bahrain',
                                   grepl('bangladesh', country, ignore.case = T) ~ 'Bangladesh',
                                   grepl('botswana', country, ignore.case = T) ~ 'Botswana',
                                   grepl('camer', country, ignore.case = T) ~ 'Cameroon',
                                   grepl('المغرب', country, ignore.case = T) | grepl('Maroc', country, ignore.case = T) ~ 'Morocco',
                                   grepl('jordan', country, ignore.case = T) ~ 'Jordan',
                                   grepl('ليبيا', country, ignore.case = T) ~ 'Libya',
                                   grepl('مصر', country, ignore.case = T) ~ 'Egypt',
                                   grepl('mark', country, ignore.case = T) ~ 'Denmark',
                                   grepl('salvador', country, ignore.case = T) ~ 'El Salvador',
                                   grepl('estonia', country, ignore.case = T) ~ 'Estonia',
                                   grepl('korea', country, ignore.case = T) | grepl('Güney Kore', country, ignore.case = T) ~ 'South Korea',
                                   grepl('hungary', country, ignore.case = T) ~ 'Hungary',
                                   grepl('maurice', country, ignore.case = T) ~ 'Mauritius',
                                   grepl('jamaica', country, ignore.case = T) ~ 'Jamaica',
                                   grepl('kenia', country, ignore.case = T) ~ 'Kenya',
                                   grepl('laos', country, ignore.case = T) ~ 'Laos',
                                   grepl('latvia', country, ignore.case = T) ~ 'Latvia',
                                   grepl('malta', country, ignore.case = T) ~ 'Malta',
                                   grepl('myanmar', country, ignore.case = T) ~ 'Myanmar',
                                   grepl('nepal', country, ignore.case = T) ~ 'Nepal',
                                   grepl('^oman$', country, ignore.case = T) ~ 'Oman',
                                   grepl('qatar', country, ignore.case = T) ~ 'Qatar',
                                   grepl('panam', country, ignore.case = T) ~ 'Panama',
                                   grepl('tanzania', country, ignore.case = T) ~ 'United Republic of Tanzania',
                                   grepl('vietnam', country, ignore.case = T) ~ 'Vietnam'))
# tallying un-recoded countries to see missing ones
country_counts <- dt5newVars %>%
  filter(is.na(coded_country)) %>%
  group_by(country) %>%
  tally()

Action needed:
We currently have 982 different free text country responses. The most recent codes leaves 1313 responses are still not consolidated.

Political Orientation

Political orientation was measured per language. We merge these variables here.

# clean-up country coding:
rm(country_counts)

# political orientation
dt5newVars <- dt5newVars %>%
  mutate(PolOrX = labelled(rowSums(dplyr::select(., ends_with("_x")), na.rm = T), 
                           labels = NULL, label="Political Compass X-Coordinate"),
         PolOrY = labelled(rowSums(dplyr::select(., ends_with("_y")), na.rm = T), 
                           labels = NULL, label="Political Compass Y-Coordinate"),
         PolOrAuthoritarianLeft = rowSums(dplyr::select(., ends_with("_Authoritarian_Left")), na.rm = T),
         PolOrAuthoritarianLeftLab = dplyr::recode(PolOrAuthoritarianLeft, `1` =  "Authoritarian Left", `0` = ""),
         PolOrAuthoritarianRight = rowSums(dplyr::select(., ends_with("_Authoritarian_right")), na.rm = T),
         PolOrAuthoritarianRightLab = dplyr::recode(PolOrAuthoritarianRight, `1` =  "Authoritarian Right", `0` = ""),
         PolOrLibertarianLeft = rowSums(dplyr::select(., ends_with("_Libertarian_Left")), na.rm = T),
         PolOrLibertarianLeftLab = dplyr::recode(PolOrLibertarianLeft, `1` =  "Libertarian Left", `0` = ""),
         PolOrLibertarianRight = rowSums(dplyr::select(., ends_with("_Libertarian_Right")), na.rm = T),
         PolOrLibertarianRightLab = dplyr::recode(PolOrLibertarianRight, `1` =  "Libertarian Right", `0` = ""),
         PolOrOther = rowSums(dplyr::select(., ends_with("_Other")), na.rm = T),
         PolOrOtherLab = dplyr::recode(PolOrOther, `1` =  "Other", `0` = ""),
         PolOrCat = paste0(PolOrAuthoritarianLeftLab, 
                           PolOrAuthoritarianRightLab,
                           PolOrLibertarianLeftLab,
                           PolOrLibertarianRightLab,
                           PolOrOtherLab), 
         PolOrCat = as.factor(na_if(PolOrCat, ""))) %>%
  dplyr::select(-starts_with("Pol"),
         PolOrX,
         PolOrY,
         PolOrCat)
attr(dt5newVars$PolOrCat,'label') <- 'Political Orientation Quadrant'

Affect

High Arousal Negative

# High Arousal Negative
## Anger not measured in wave 1
pairs.panels.new(dt5newVars %>% dplyr::select(affAnx, affNerv))

cat("<br>")


dt5newVars$affHighNeg.m <- scoreItems(keys=c(1,1), items = dt5newVars %>% dplyr::select(affAnx, affNerv), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt5newVars$affHighNeg.m, skew=F)) %>%
  mutate(vars = "High Arousal Negative Affect") %>%
  kable(., caption = "High Arousal Negative Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Negative Affect: Scale Descriptives
vars n mean sd min max range se
High Arousal Negative Affect 22140 2.893 0.9753 1 5 4 0.0066 
dt5newVars$affHighNeg.c <- scale(dt5newVars$affHighNeg.m, scale = F, center = T)
dt5newVars$affHighNeg.z <- scale(dt5newVars$affHighNeg.m, scale = T)
dt5newVars$affHighNeg.fa <- fa(dt5newVars %>% dplyr::select(affAnx, affNerv))$scores

Low Arousal Negative Affect

# Low Arousal Negative Affect
ia.affLowNeg <- dt5newVars %>%
    dplyr::select(affBor, affExh, affDepr) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affLowNeg$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.57 . Furthermore, deleting item(s) 1 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affLowNeg)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affDepr 0.4667 0.8076 2.212 1.154
affExh 0.3811 0.5210 2.519 1.226
affBor 0.2932 0.3720 2.539 1.302 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(affBor, affExh, affDepr))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Low Arousal Negative Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Negative Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affBor 16901 2.539 1.302 2 2.442 1.483 1 5 4 0.3597 -1.0522 0.0100
affExh 16860 2.519 1.226 2 2.452 1.483 1 5 4 0.3142 -0.9974 0.0094
affDepr 16890 2.212 1.154 2 2.096 1.483 1 5 4 0.6405 -0.5863 0.0089 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(affBor, affExh, affDepr))

cat("<br>")


dt5newVars$affLowNeg.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(affBor, affExh, affDepr), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt5newVars$affLowNeg.m, skew=F)) %>%
  mutate(vars = "Low Arousal Negative Affect") %>%
  kable(., caption = "Low Arousal Negative Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Negative Affect: Scale Descriptives
vars n mean sd min max range se
Low Arousal Negative Affect 22140 2.323 0.8046 1 5 4 0.0054 
dt5newVars$affLowNeg.c <- scale(dt5newVars$affLowNeg.m, scale = F, center = T)
dt5newVars$affLowNeg.z <- scale(dt5newVars$affLowNeg.m, scale = T)
dt5newVars$affLowNeg.fa <- fa(dt5newVars %>% dplyr::select(affBor, affExh, affDepr))$scores

Low Arousal Positive Affect

# Low Arousal Positive Affect
ia.affLowPos <- dt5newVars %>%
    dplyr::select(affCalm, affContent, affRel) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affLowPos$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.73 . Furthermore, deleting item(s) 2 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affLowPos)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affRel 0.6214 0.8062 2.553 1.093
affCalm 0.6099 0.7786 2.808 1.067
affContent 0.4476 0.5096 2.570 1.091 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(affCalm, affContent, affRel))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Low Arousal Positive Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Positive Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affCalm 16881 2.808 1.067 3 2.831 1.483 1 5 4 -0.0229 -0.7370 0.0082
affContent 16846 2.570 1.091 3 2.548 1.483 1 5 4 0.1307 -0.8139 0.0084
affRel 16864 2.553 1.093 3 2.525 1.483 1 5 4 0.1821 -0.7993 0.0084 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(affCalm, affContent, affRel))

cat("<br>")


dt5newVars$affLowPos.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(affCalm, affContent, affRel), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt5newVars$affLowPos.m, skew=F)) %>%
  mutate(vars = "Low Arousal Positive Affect") %>%
  kable(., caption = "Low Arousal Positive Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Positive Affect: Scale Descriptives
vars n mean sd min max range se
Low Arousal Positive Affect 22140 2.728 0.779 1 5 4 0.0052 
dt5newVars$affLowPos.c <- scale(dt5newVars$affLowPos.m, scale = F, center = T)
dt5newVars$affLowPos.z <- scale(dt5newVars$affLowPos.m, scale = T)
dt5newVars$affLowPos.fa <- fa(dt5newVars %>% dplyr::select(affCalm, affContent, affRel))$scores

High Arousal Positive Affect

# High Arousal Positive Affect
ia.affHighPos <- dt5newVars %>%
    dplyr::select(affEnerg, affExc, affInsp) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affHighPos$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.68 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affHighPos)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affInsp 0.5291 0.7205 2.323 1.136
affEnerg 0.4952 0.6447 2.421 1.074
affExc 0.4542 0.5694 2.097 1.099 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(affEnerg, affExc, affInsp))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "High Arousal Positive Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Positive Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affEnerg 16862 2.421 1.074 2 2.369 1.483 1 5 4 0.2566 -0.7370 0.0083
affExc 16834 2.097 1.099 2 1.967 1.483 1 5 4 0.6779 -0.4938 0.0085
affInsp 16863 2.323 1.136 2 2.239 1.483 1 5 4 0.4316 -0.7707 0.0087 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(affEnerg, affExc, affInsp))

cat("<br>")


dt5newVars$affHighPos.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(affEnerg, affExc, affInsp), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt5newVars$affHighPos.m, skew=F)) %>%
  mutate(vars = "High Arousal Positive Affect") %>%
  kable(., caption = "High Arousal Positive Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Positive Affect: Scale Descriptives
vars n mean sd min max range se
High Arousal Positive Affect 22140 2.213 0.7636 1 5 4 0.0051 
dt5newVars$affHighPos.c <- scale(dt5newVars$affHighPos.m, scale = F, center = T)
dt5newVars$affHighPos.z <- scale(dt5newVars$affHighPos.m, scale = T)
dt5newVars$affHighPos.fa <- fa(dt5newVars %>% dplyr::select(affEnerg, affExc, affInsp))$scores

Loneliness

ia.lone<- dt5newVars %>%
    dplyr::select(starts_with("lone")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.lone$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.8 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.lone)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
lone01 0.6829 0.8197 2.340 1.130
lone02 0.6438 0.7490 2.625 1.216
lone03 0.6187 0.7086 1.941 1.088 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("lone")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Loneliness: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Loneliness: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
lone01 16420 2.340 1.130 2 2.260 1.483 1 5 4 0.3903 -0.7852 0.0088
lone02 16408 2.625 1.216 3 2.578 1.483 1 5 4 0.1722 -0.9937 0.0095
lone03 16401 1.941 1.088 2 1.770 1.483 1 5 4 0.9577 0.0113 0.0085 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("lone")))

cat("<br>")


dt5newVars$lone.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(starts_with("lone")), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt5newVars$lone.m, skew=F)) %>%
  mutate(vars = "Loneliness") %>%
  kable(., caption = "Loneliness: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Loneliness: Scale Descriptives
vars n mean sd min max range se
Loneliness 22140 2.31 0.8372 1 5 4 0.0056 
dt5newVars$lone.c <- scale(dt5newVars$lone.m, scale = F, center = T)
dt5newVars$lone.z <- scale(dt5newVars$lone.m, scale = T)
dt5newVars$lone.fa <- fa(dt5newVars %>% dplyr::select(starts_with("lone")))$scores

Boredom

ia.bor<- dt5newVars %>%
    dplyr::select(starts_with("bor0"), -bor03) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.bor$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.5 . Item(s) that exhibited low correlation with the rest of the scale were: 3 . Furthermore, deleting item(s) 3 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.bor)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
bor02 0.4836 0.7197 0.0578 1.874
bor01 0.4529 0.7097 0.3892 1.900
bor03_R 0.0761 0.4390 -0.2860 1.677 
pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("bor0"), -bor03))

Item dropped:
Item three was not well behaved. It seems to measure something else. We dropped it for now.

pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("bor0"), -bor03, -bor03_R))

cat("<br>")


dt5newVars$bor.m <- scoreItems(keys=c(1,1), items = dt5newVars %>% dplyr::select(starts_with("bor0"), -bor03, -bor03_R), min = -3, max = 3)$scores

as.data.frame(psych::describe(dt5newVars$bor.m, skew=F)) %>%
  mutate(vars = "Boredom") %>%
  kable(., caption = "Boredom: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Boredom: Scale Descriptives
vars n mean sd min max range se
Boredom 22140 0.1559 1.42 -3 3 6 0.0095 
dt5newVars$bor.c <- scale(dt5newVars$bor.m, scale = F, center = T)
dt5newVars$bor.z <- scale(dt5newVars$bor.m, scale = T)
dt5newVars$bor.fa <- fa(dt5newVars %>% dplyr::select(starts_with("bor0"), -bor03, -bor03_R))$scores

Isolation

cat(crayon::bold("Offline Isolation"))

Offline Isolation

ia.isoPers <- dt5newVars %>%
    dplyr::select(ends_with("inPerson")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.isoPers$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.56 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.isoPers)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
isoOthPpl_inPerson 0.4466 0.7590 1.9215 2.204
isoImmi_inPerson 0.3545 0.4859 0.4502 1.357
isoFriends_inPerson 0.3226 0.4288 2.0844 2.490 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(ends_with("inPerson")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Isolation offline: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation offline: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
isoFriends_inPerson 16310 2.0844 2.490 1 1.7305 1.483 0 7 7 0.9653 -0.5160 0.0195
isoOthPpl_inPerson 16220 1.9215 2.204 1 1.5538 1.483 0 7 7 1.0752 0.0131 0.0173
isoImmi_inPerson 16110 0.4502 1.357 0 0.0582 0.000 0 7 7 3.4731 11.7176 0.0107 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(ends_with("inPerson")))

cat("<br>")


dt5newVars$isoPers.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(ends_with("inPerson")), min = 0, max = 7)$scores

as.data.frame(psych::describe(dt5newVars$isoPers.m, skew=F)) %>%
  mutate(vars = "Isolation offline") %>%
  kable(., caption = "Isolation offline: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation offline: Scale Descriptives
vars n mean sd min max range se
Isolation offline 22140 1.267 1.345 0 7 7 0.009 
dt5newVars$isoPers.c <- scale(dt5newVars$isoPers.m, scale = F, center = T)
dt5newVars$isoPers.z <- scale(dt5newVars$isoPers.m, scale = T)
dt5newVars$isoPers.fa <- fa(dt5newVars %>% dplyr::select(ends_with("inPerson")))$scores

cat(crayon::bold("Online Isolation"))

Online Isolation

ia.isoOnl <- dt5newVars %>%
    dplyr::select(ends_with("online")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.isoOnl$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.53 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.isoOnl)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
isoOthPpl_online 0.4547 0.9247 3.0002 2.726
isoFriends_online 0.2990 0.3797 4.8748 2.346
isoImmi_online 0.2872 0.3620 0.7673 1.844 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(ends_with("inPerson")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Isolation online: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation online: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
isoFriends_inPerson 16310 2.0844 2.490 1 1.7305 1.483 0 7 7 0.9653 -0.5160 0.0195
isoOthPpl_inPerson 16220 1.9215 2.204 1 1.5538 1.483 0 7 7 1.0752 0.0131 0.0173
isoImmi_inPerson 16110 0.4502 1.357 0 0.0582 0.000 0 7 7 3.4731 11.7176 0.0107 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(ends_with("online")))

cat("<br>")


dt5newVars$isoOnl.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(ends_with("online")), min = 0, max = 7)$scores

as.data.frame(psych::describe(dt5newVars$isoPers.m, skew=F)) %>%
  mutate(vars = "Isolation online") %>%
  kable(., caption = "Isolation online: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation online: Scale Descriptives
vars n mean sd min max range se
Isolation online 22140 1.267 1.345 0 7 7 0.009 
dt5newVars$isoOnl.c <- scale(dt5newVars$isoOnl.m, scale = F, center = T)
dt5newVars$isoOnl.z <- scale(dt5newVars$isoOnl.m, scale = T)
dt5newVars$isoOnl.fa <- fa(dt5newVars %>% dplyr::select(ends_with("online")))$scores

# Leave House
as.data.frame(psych::describe(dt5newVars$houseLeave, skew=F)) %>%
  mutate(vars = "Leaving House") %>%
  kable(., caption = "Leaving House: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Leaving House: Item Descriptive
vars n mean sd min max range se
Leaving House 15538 2.434 1.066 1 4 3 0.0086

Government Response

No responses yet

# extC19Msg

Community Response

ia.ext <- dt5newVars %>%
    dplyr::select(starts_with("extC19"), -extC19Msg) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.ext$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.77 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.ext)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
extC19Rules 0.7057 0.9249 4.076 1.451
extC19Org 0.5819 0.6703 3.763 1.443
extC19Punish 0.5413 0.6108 3.198 1.670 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("extC19"), -extC19Msg))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Community response: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Community response: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
extC19Rules 15789 4.076 1.451 4 4.168 1.483 1 6 5 -0.4310 -0.7039 0.0115
extC19Punish 15782 3.198 1.670 3 3.123 1.483 1 6 5 0.1874 -1.1864 0.0133
extC19Org 15789 3.763 1.443 4 3.800 1.483 1 6 5 -0.1957 -0.8002 0.0115 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("extC19"), -extC19Msg))

cat("<br>")


dt5newVars$ext.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(starts_with("extC19"), -extC19Msg), 
                             min = 1, max = 6)$scores

as.data.frame(psych::describe(dt5newVars$ext.m, skew=F)) %>%
  mutate(vars = "Community response") %>%
  kable(., caption = "Community response: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Community response: Scale Descriptives
vars n mean sd min max range se
Community response 22140 3.675 1.066 1 6 5 0.0072 
dt5newVars$ext.c <- scale(dt5newVars$ext.m, scale = F, center = T)
dt5newVars$ext.z <- scale(dt5newVars$ext.m, scale = T)
dt5newVars$ext.fa <- fa(dt5newVars %>% dplyr::select(starts_with("extC19"), -extC19Msg))$scores

Behavioral Response

ia.beh <- dt5newVars %>%
    dplyr::select(starts_with("c19per")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.beh$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.68 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.beh)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
c19perBeh02 0.6014 0.8971 2.616 0.8676
c19perBeh03 0.4663 0.5656 2.072 1.3721
c19perBeh01 0.4302 0.5106 2.464 0.9582 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("c19per")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Behavioral response: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Behavioral response: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
c19perBeh01 15882 2.464 0.9582 3 2.680 0 -3 3 6 -2.855 10.696 0.0076
c19perBeh02 15885 2.616 0.8676 3 2.809 0 -3 3 6 -3.616 16.716 0.0069
c19perBeh03 15887 2.072 1.3721 3 2.375 0 -3 3 6 -1.874 3.305 0.0109 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("c19per")))

cat("<br>")


dt5newVars$beh.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(starts_with("c19per")), 
                             min = -3, max = 3)$scores

as.data.frame(psych::describe(dt5newVars$beh.m, skew=F)) %>%
  mutate(vars = "Behavioral response") %>%
  kable(., caption = "Behavioral response: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Behavioral response: Scale Descriptives
vars n mean sd min max range se
Behavioral response 22140 2.558 0.7658 -3 3 6 0.0051 
dt5newVars$beh.c <- scale(dt5newVars$beh.m, scale = F, center = T)
dt5newVars$beh.z <- scale(dt5newVars$beh.m, scale = T)
dt5newVars$beh.fa <- fa(dt5newVars %>% dplyr::select(starts_with("c19per")))$scores

Hope and Efficacy

as.data.frame(psych::describe(dt5newVars$c19Hope)) %>%
  mutate(vars = "Hope") %>%
  kable(., caption = "Hope: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Hope: Item Descriptive
vars n mean sd median trimmed mad min max range skew kurtosis se
Hope 15939 1.096 1.6 1 1.279 1.483 -3 3 6 -0.8269 -0.1207 0.0127 
ggplot(dt5newVars, aes(x = c19Hope)) +
  geom_histogram(binwidth=1, alpha=0.5) +
  #geom_density(alpha=0.6)+
  labs(title="Hope distribution",x="Corona Virus Hope", y = "Frequency") +
  theme_Publication()

as.data.frame(psych::describe(dt5newVars$c19Eff)) %>%
  mutate(vars = "Efficacy") %>%
  kable(., caption = "Efficacy: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Efficacy: Item Descriptive
vars n mean sd median trimmed mad min max range skew kurtosis se
Efficacy 15934 0.7955 1.654 1 0.9245 1.483 -3 3 6 -0.6409 -0.473 0.0131 
ggplot(dt5newVars, aes(x = c19Eff)) +
  geom_histogram(binwidth=1, alpha=0.5) +
  #geom_density(alpha=0.6)+
  labs(title="Efficacy distribution",x="Corona Virus Efficacy", y = "Frequency") +
  theme_Publication()

State Paranoia

ia.para <- dt5newVars %>%
    dplyr::select(starts_with("para")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.para$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.7 . Furthermore, deleting item(s) 1 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.para)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
para02 0.6219 0.8830 2.395 2.591
para03 0.5557 0.7007 2.305 2.597
para01 0.3854 0.4379 5.356 3.002 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("para")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "State Paranoia: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
State Paranoia: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
para01 15175 5.356 3.002 6 5.438 2.965 0 10 10 -0.2625 -0.9413 0.0244
para02 15167 2.395 2.591 2 2.009 2.965 0 10 10 1.0120 0.2084 0.0210
para03 15162 2.305 2.597 1 1.900 1.483 0 10 10 1.0575 0.2242 0.0211 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("para")))

cat("<br>")


dt5newVars$para.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(starts_with("para")), 
                             min = 0, max = 10)$scores

as.data.frame(psych::describe(dt5newVars$para.m, skew=F)) %>%
  mutate(vars = "State Paranoia") %>%
  kable(., caption = "State Paranoia: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
State Paranoia: Scale Descriptives
vars n mean sd min max range se
State Paranoia 22140 3.241 1.795 0 10 10 0.0121 
dt5newVars$para.c <- scale(dt5newVars$para.m, scale = F, center = T)
dt5newVars$para.z <- scale(dt5newVars$para.m, scale = T)
dt5newVars$para.fa <- fa(dt5newVars %>% dplyr::select(starts_with("para")))$scores

Conspiracy Theory

ia.consp <- dt5newVars %>%
    dplyr::select(starts_with("consp")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.consp$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.7 . Furthermore, deleting item(s) 3 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.consp)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
consp02 0.6002 0.8209 7.200 2.476
consp01 0.5796 0.7630 6.893 2.621
consp03 0.3757 0.4279 5.277 2.726 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("consp")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Conspiracy Theory: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Conspiracy Theory: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
consp01 14892 6.893 2.621 7 7.173 2.965 0 10 10 -0.6862 -0.2324 0.0215
consp02 14877 7.200 2.476 8 7.503 2.965 0 10 10 -0.8609 0.1864 0.0203
consp03 14877 5.277 2.726 5 5.317 2.965 0 10 10 -0.1088 -0.7469 0.0224 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("consp")))

cat("<br>")


dt5newVars$consp.m <- scoreItems(keys=c(1,1,1), items = dt5newVars %>% dplyr::select(starts_with("consp")), 
                             min = 0, max = 10)$scores

as.data.frame(psych::describe(dt5newVars$consp.m, skew=F)) %>%
  mutate(vars = "Conspiracy Theory") %>%
  kable(., caption = "Conspiracy Theory: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Conspiracy Theory: Scale Descriptives
vars n mean sd min max range se
Conspiracy Theory 22140 6.526 1.694 0 10 10 0.0114 
dt5newVars$para.c <- scale(dt5newVars$consp.m, scale = F, center = T)
dt5newVars$para.z <- scale(dt5newVars$consp.m, scale = T)
dt5newVars$para.fa <- fa(dt5newVars %>% dplyr::select(starts_with("consp")))$scores

Job Insecurity

ia.jobinsec<- dt5newVars %>%
    dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>%
    na_if(., -99) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.jobinsec$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.85 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.jobinsec)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
jbInsec01 0.7559 0.8731 -0.7854 1.168
jbInsec03 0.7102 0.7952 -0.0872 1.374
jbInsec02_R 0.6822 0.7527 -0.7252 1.157 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Job insecurity: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Job insecurity: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
jbInsec01 12033 -0.7854 1.168 -1 -0.9279 1.483 -2 2 4 0.7725 -0.2330 0.0106
jbInsec03 13081 -0.0872 1.374 0 -0.1090 1.483 -2 2 4 0.0364 -1.2885 0.0120
jbInsec02_R 12439 -0.7252 1.157 -1 -0.8496 1.483 -2 2 4 0.6760 -0.3479 0.0104 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99))

cat("<br>")


dt5newVars$jobinsec.m <- scoreItems(keys=c(1,1,1), 
                                  items = dt5newVars %>% dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99),
                                  min = -2, max = 2)$scores

as.data.frame(psych::describe(dt5newVars$jobinsec.m, skew=F)) %>%
  mutate(vars = "Job insecurity") %>%
  kable(., caption = "Job insecurity: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Job insecurity: Scale Descriptives
vars n mean sd min max range se
Job insecurity 22140 -0.5935 0.7891 -2 2 4 0.0053 
dt5newVars$jobinsec.c <- scale(dt5newVars$jobinsec.m, scale = F, center = T)
dt5newVars$jobinsec.z <- scale(dt5newVars$jobinsec.m, scale = T)
dt5newVars$jobinsec.fa <- fa(dt5newVars %>% dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04))$scores

Financial Strain

ia.pfs<- dt5newVars %>%
    dplyr::select(starts_with("PFS0")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.pfs$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.85 . Furthermore, deleting item(s) 2 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.pfs)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
PFS01 0.7983 0.9333 -0.2230 1.238
PFS03 0.7589 0.8586 -0.4758 1.241
PFS02 0.6157 0.6522 0.4220 1.205 
cat("<br>")


as.data.frame(psych::describe(dt5newVars %>% dplyr::select(starts_with("PFS0")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Financial Strain: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Financial Strain: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
PFS01 16367 -0.2230 1.238 0 -0.2711 1.483 -2 2 4 0.1712 -1.0222 0.0097
PFS02 16378 0.4220 1.205 1 0.5161 1.483 -2 2 4 -0.5615 -0.6736 0.0094
PFS03 16374 -0.4758 1.241 -1 -0.5645 1.483 -2 2 4 0.4389 -0.8831 0.0097 
cat("<br>")


pairs.panels.new(dt5newVars %>% dplyr::select(starts_with("PFS0")))

cat("<br>")


dt5newVars$pfs.m <- scoreItems(keys=c(1,1,1), 
                                  items = dt5newVars %>% dplyr::select(starts_with("PFS0")),
                                  min = -2, max = 2)$scores

as.data.frame(psych::describe(dt5newVars$pfs.m, skew=F)) %>%
  mutate(vars = "inancial Strain") %>%
  kable(., caption = "inancial Strain: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
inancial Strain: Scale Descriptives
vars n mean sd min max range se
inancial Strain 22140 -0.0682 0.9295 -2 2 4 0.0062 
dt5newVars$pfs.c <- scale(dt5newVars$pfs.m, scale = F, center = T)
dt5newVars$pfs.z <- scale(dt5newVars$pfs.m, scale = T)
dt5newVars$pfs.fa <- fa(dt5newVars %>% dplyr::select(starts_with("PFS0")))$scores

Reduce to Relevant Variales

# clean-up Item Analyses
rm(list=ls(pattern="ia"))

# remove directly identifiable data (with and without page timers)
dt6ReducedTimer <- dt5newVars %>%
  dplyr::select(-c(IPAddress, 
            RecipientLastName, 
            RecipientFirstName, 
            RecipientEmail, 
            ExternalReference, 
            LocationLatitude, 
            LocationLongitude,
            DistributionChannel,
            ICRec_1_TEXT))
dt6Reduced <- dt6ReducedTimer %>%
  dplyr::select(-starts_with("t_"))

# remove filtered cases (with and without page timers)
dt6ReducedTimerCases <- dt6ReducedTimer %>%
  filter(FilterPreview == 0,
         FilterTime == 0,
         FilterStraightliner == 0) %>%
  dplyr::select(-starts_with("Filter"))
dt6ReducedCases <- dt6Reduced %>%
  filter(FilterPreview == 0,
         FilterTime == 0,
         FilterStraightliner == 0) %>%
  dplyr::select(-starts_with("Filter"))

Export

Export main dataframe as RData and SPSS sav files. We export versions with and without page timers

namSPSS <- paste0("data/cleaned data/Psycorona Baseline cleaned ", format(Sys.time(), format = "%F %H-%M %Z"),".sav")
namR <- paste0("data/cleaned data/Psycorona Baseline cleaned ", format(Sys.time(), format = "%F %H-%M %Z"),".RData")
namTSPSS <- paste0("data/cleaned data/Psycorona Baseline cleaned with page timer ", format(Sys.time(), format = "%F %H-%M %Z"),".sav")
namTR <- paste0("data/cleaned data/Psycorona Baseline cleaned with page timer ", format(Sys.time(), format = "%F %H-%M %Z"),".RData")
write_sav(dt6Reduced, namSPSS)
write_sav(dt6ReducedTimer, namTSPSS)
save(dt6Reduced, file = namR)
save(dt6ReducedTimer, file = namTR)
rm(list=ls(pattern="nam"))

# export for Shiny
saveRDS(dt6ReducedCases, file = "../PsyCorona-WebApp/data/reducedData.rds")